Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context beyond annotations: it fetches and scrapes the page, detects from structured data plus text, and includes a low-confidence branch that requires asking the user. No contradiction exists between description and annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the most important usage instruction, then lists outputs and the critical conditional behavior. Every sentence adds distinct value, and the bolded low-confidence instruction is appropriately emphasized without unnecessary length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists and the input schema is fully documented, the description is complete: it explains when to call it, what it does, what it returns, and how to handle low-confidence detections. An agent has everything needed to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema coverage is 100%, so the schema already documents all three parameters clearly. The description adds little parameter-specific meaning beyond what the schema states, though it does clarify the detection data sources. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific purpose: detect the site's business vertical from JSON-LD schema and scraped text, and frame it for the user's business. It also names concrete outputs (AI-search lift %, competitor adoption %, positioning pitch) and distinguishes the tool from scan_site by describing its post-scan analytical role.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use it immediately after scan_site, and gives a clear conditional: if should_ask_user is true, ask the user rather than acting on the guessed vertical. This provides strong when-to-use and when-not-to-continue guidance without ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.